A Proposal Framework for Evaluating the Impact of Electronic Customer Relationship Management in Telecommunication Industry in Jordan Abdel Fattah Mahmoud Al-Azzam Electronic customer relationship management is a marketing strategy and integrated approach that is applied to identifying, acquiring, and retaining valuable customers, enabling organization to manage and coordinate customer interaction across multiple channels, provide appropriate service and products to satisfy customers so as to retain customer loyalty and enhance customer profitability. E-CRM is a combination of hardware, software, process, application and management to strength relationship between customers s and service providers. This research investigates the relationship between E-CRM performance and customer loyalty in Jordanian telecommunication industry. Furthermore, this research also examined the influence of customers, process and technology factors on E-CRM performance. A total of 500 university students in Jordan participated in this research by voluntarily completing the survey questionnaire. Using regression analysis, the results supported that e-crm performance significantly related to customer loyalty. Five major antecedent factors were found to have significant influence on E-CRM performance that is customer commitment, customer privacy, customer trust, ease of use, and E- service quality. The theoretical implications and managerial implications of these findings are discussed. Track: Marketing Keyword: Customer loyalty, E-customer relationship management (CRM), customer commitment, customer privacy, and telecommunication industry in Jordan. 1. Introduction Currently, the telecommunication industry is changing rapidly and widely growing and becoming one of the major communication channels for the day-to-day life in Jordan. Furthermore, communication is becoming the nerve of the life. In particular, mobile phone service is gaining popularity and significance all around the world and as mobile usage is growing rapidly, telecommunication marketers are developing new strategies to take advantage of the potential customers. Not only the rapidly development and improvement but also high competition in the mobile phone industry has resulted in the three major players in the Jordan market, including Orange, Zain, and Umniah service provider. These three market leaders had long been competing in various aspects with broad range of competitive strategies. As a result of the high competition in this market, mobile phone users in Jordan have been influenced by the marketing strategies of these three major competitors. Assistant professor Dr. Abdel Fattah Mahmoud Al-Azzam, Head of Department marketing, Zarqa University,Jordan, E-mail: abdel_fattah1963@yahoo.com This research is funded by Deanship scientific research and Graduate studies Zarga University, Jordan.
Thus, Companies including communication companies are putting much more emphasis on customer relationship management (CRM) as a tool for managing customer relationship and to increase customer satisfaction and loyalty. E-CRM emerges from the internet and web technology to facilitate the implementation of CRM. Therefore, by implementing e-crm, they can provide services based on customer need and expectations (Boohene, & Agyapong, 2011). However, despite the importance of CRM to the success of the organization, empirical studies on e-crm performance particularly in the Jordanian context have been less than encouraging. Furthermore, little systematic effort been taken to understand the underlying consequences of e-crm performance. Therefore, an empirical study is needed to overcome this shortcoming. Finally, According to Samsudin et al., (2010) there is lack of literature on E-CRM and more studies work is needed in this area. This study will discuss the e- service quality, ease of use, customer commitment, customer privacy, and customer trust as antecedents of E-CRM performance in Jordan Mobile Phone Services by reviewing the conceptual article and research findings. 2. Literature Review A review of work in the area of e-crm performance indicates that a little research have investigated the antecedent of e-crm performance. The previous studies on the antecedent of e-crm performance come from organization factors such as, company policy, business process and strategies. As we understand, the main objective of e-crm activities is to create customer loyalty and customer value. Based on the above discussions, there are several factors that may influence e-crm performance. It is clear that not all potential factors can be included in the present study. Only those that are relevant with e-crm performance and mobile phone service from customer perspective were chosen. 2.1 Electronic Customer Relationship Management E-CRM Electronic customer relationship management (e-crm) is a new phenomenon within an electronic commerce (marketing) that has increased its importance dramatically over the last few years, and will continue to do in the future. E-CRM exists to replace the traditional 'four Ps' of marketing (product, price, place and promotion) (Nor Azila & Mohd Noor, 2011). Furthermore, a number of studies suggested that the 4Ps of marketing mix is no longer the dominant marketing logic and that relationship marketing may be a more appropriate new paradigm for marketing thought, theory and practice (Gura, 2003; Nor Azila & Mohd Noor, 2011).Therefore, e-crm is designed for people at all levels in business who wants to develop relationships with customers electronically. Because of that, it is critical to understand the important role that e-crm plays within modern marketing organizations (Usman,Jalal, & Musa, 2012). Currently, the primary focus of study has centered on the impact of e-crm performance from customers and organizational perspective. Studies discovered numerous positive outcomes of e-crm performance such as customer satisfaction (Khalifa,& Shen, 2005; Usman, et al., 2012), purchase intentions (Khalifa,& Shen, 2005, Wang et al., 2004), customer retention [Jayachandran et al., 2005), knowledge management (Donio et al., 2006), profitability (Kim et al., 2004), relationship
development (Rabbai, 2012), organizational commitment (Keshvari, 2011; Hamid et al., 2011) and customer loyalty (Khaligh et al., 2012; Chang et al., 2005). 2.2 Customer Commitment One of the most important factors to influence e-crm performance is customer commitment. This variable is considered in marketing literature as a significant factor necessary for the creation, building and maintenance of relationship (Al momani, & Norazila, 2009).The notion of customer commitment has been introduced and used widely in marketing and organizational environment. For instance, Aydin, and Ozer (2005) considered behavioral commitment as a critical variable to differentiate social from economic exchange. But little is known about the potential role of commitment in e-commerce and hence e-crm performance (Dimitrioder, 2006). Despite the fact that commitment is a central construct in the area of relationship marketing, there is little agreement on the nature of this construct. Customer commitment is defined as a desire to maintain a relationship with service provider (Nor Azila & Mohd Noor, 2011). Lee, (2003), defined commitment as a psychological state generated by an individual s perception, beliefs and emotions which aggravate the willingness or intention of developing and maintaining a stable relationship. The various definitions make it difficult to determine what commitment means to customers and organization. Furthermore, the successful relationship marketing requires relationship commitment between buyers and seller. Therefore, commitment is central to all the relational exchanges between the customer and service providers. Furthermore, the present study hypothesizes that customer commitment influences significantly e- CRM. This is because some prior studies demonstrate significant results in this regard. Hence, based on the above arguments, the following hypothesis is offered. Hypothesis 1: There is a significant positive relationship between customer commitment and e-crm. 2.3 Customer Privacy Another customer factor that has been chosen as antecedent variable in the present research is customer privacy. Privacy is a difficult concept to describe and define since privacy has been used to demonstrate a wide number of interests including personal information control, reproductive autonomy, access to places and bodies, secrecy, and personal development (Samsudin, et al, 2011). Furthermore, there are numerous definitions and measures of privacy, but there is no consensus on a single definition. Samsudin Wahab et al., (2011), defined privacy as the right of an individual to control the information held about them by third parties. Furthermore, privacy can be also defined as the people right to be secure in their persons, houses, papers, and effects against unreasonable searches and seizures, shall not be violated, and no warrants shall issue, but upon probable cause, supported by oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized (Rabba, 2012). Besides, AlShaali & Varshney (2005), identified privacy as a key issue that internet marketers need to recognize and address. Finally, it is clearly that information security and privacy is the most critical problem faces the
dealers over the Internet. Study by Casalo, et al., (2008), explore that the customers have very strong views about protecting their privacy on the internet. Based on the above literature we notice that privacy is a significant CRM concern, and it is clear that privacy is very important factor influencing e- businesses and e- commerce. Hence, based on the above arguments, the following hypothesis is offered. Hypothesis 2: There is a significant positive relationship between customer privacy and e-crm. 2.4 Customer Trust Trust also appears to be an important antecedent to e-crm. Taylor, & Hunter, (2003), defines trust as the expectation that arises within a community of regular, honest, and cooperative behavior, based on commonly shared norms, on the part of members of that community. Taylor, & Hunter, argues that the technological revaluation will make trust ever more important in understanding business behaviors (like marketing). Trust has been defined as users thoughts, feeling, emotion, or behaviors that occur when customers feel that the provider can be relied upon to act in their best interest when they give up direct control (Mukherjee, & Nath,2007). Moreover, trust is important in the online environment, affecting customer s perception and willingness to participate and establish relationships with suppliers online (Aniba, 2011; Samsudin Wahab, 2011). Moreover, Al-Momani (2009), found a positive relationship between the level of trust in the technology and customer intention to adopt e-commerce. Most researchers agree that lack of trust is a critical issue that needs addressing regarding to the internet and e-crm adoption. Lai et al., (2010), argue that lack of trust has been one of the most significant reasons for customer not adopting online services. Recently, there have been a number of empirical studies investigating the role of trust in the specific context of ecommerce. As a summary, trust is an important factor in customer attitude toward e-service and e- commerce (e-crm). This study proposes trust as one of the antecedents for e- CRM performance. Hence, based on the above arguments, the following hypothesis is offered. Hypothesis 3: There is a significant positive relationship between trust and e- CRM 2.5 Ease of Use Ease of use is another technology factor influence e-crm performance. According to Davis (1989), ease of use is the degree which a person believes that using a particular system will be free of effort. It has a strong influence on behavioral intention to adopt technology. If a technology is perceived as too difficult to use, a person will choose an alternative option that is easier for him or her to perform. It is one of the classical concepts in information systems research (Davis 1989; Sanders & Manrodt, 2003). In addition, perceive ease of use has been established in previous studies to influence customer intention and behavior, either directly or indirectly via perceived usefulness. However, some studies have linked perceive ease of use to the success and quality of
information system ( Kem, & Forsyth, 2010) as well as to customer satisfaction ( Lola, 2011). According to the previous studies on the TAM, perceived ease of use is found to have direct effect on behavioral intention (Davis, 1989). Finally, when mobile phone users perceive ease of use, they can convenient access to all varieties of business communication and services by the mobile phone anywhere and anytime then can easily and quickly schedule meeting and activities with their colleagues and get much faster search capability. In conclusion, it can be concluded that ease of use is one of the technology factor that plays significant role in e-crm performance. Hence based on the arguments, the following hypothesis is proposed. Hypothesis 4: There is a significant positive relationship between ease of use and e-crm. 2.6 E-Service Quality Beside the previous factors chosen as antecedent of e-crm performance, e- service quality is one of the components in the process factor that has been found related to e-crm performance. Generally, service quality is an important factor for profitability, and thereby service providers success. There are two fundamental processes that explain the contribution of electronic service quality to profitability for customers and service providers. Firstly, service quality is considered as one of the few means for service differentiation and competitive advantage that attracts new customers and contributes to the market share. Secondly, service quality enhance customers tendency to use the service more, to use more services, to become less price-sensitive and to tell other friends about favorable and useful service provided ( Al-Momani, & Noor, 2009). However, numerous previous studies have found a significant relationship between e-service quality and behavioral intention (Wu &Lin, 2007; Hackman etal., 2006). Perceived service quality is an overall judgment of a service that contributes to customer satisfaction, purchased intentions, and firm performance (Al-Momani, & Noor, 2009; Cronin & Taylor, 1992;). Many studies have been conducted to investigate the influence of service quality on customer satisfaction. The significance of service quality as an antecedent of customer satisfaction and eventually customer loyalty has been approved (Wal et al.,2002; Zeithaml & Malhorta, 2002). However, this research attempts to address this gap by examining electronic service quality as one of e-crm performance antecedent to explain behavior intention in mobile phone service context. Evidently, based on the earlier discussion, it is clear that e-service quality is a very important factor in influencing customer attitude toward e-service. Hence based on the arguments, the following hypothesis is proposed. Hypothesis 5: There is a significant positive relationship between E-service quality and e-crm. 2.7 Customer Loyalty Recently, competitive and changeable market place and customer loyalty are seen to be critical factors to the success of business firms because attracting new customers is more expensive than retaining exiting ones (Lee, et al., 2003; Rabbai, 2012). Numerous studies have suggested that loyal customers are a
competitive asset and that a way to retain customers is through secure and collaborative relationship between customers and service providers (Ismail, 2009). Customer loyalty has defined in numerous different but similar ways by different researchers. Khan, and Khawajo (2013) treat customer loyalty in behavioral term like repeat purchasing frequency or relative volume of samebrand purchasing. Oliver (1997) defines customer loyalty as a deeply held commitment to re-buy or re-patronize a preferred product/service consistently in the future (p.34). According to Gummeras et al., (2004), loyal customers are defined as those customers who hold favorable attitude toward a product, recommend the product to other customers and exhibit repurchase behavior. Furthermore, in this study, customer loyalty was chosen as the main consequence of e-crm performance as the previous literatures support the relationship between them. However, this change shifts competition in mobile phone service sector in Jordan from core service to value-added service. Therefore, the service providers should differentiate their services and guarantee quality of their service in order to maintain their market share and to enhance their customer satisfaction and loyalty. 2.8 Theoretical Framework This study primary focuses on exploring the antecedent of e-crm performance and its impact on customer loyalty. Based on the literature review and research problems, we develop an integrative framework that is demonstrated in Figure1. Furthermore, the framework of the present study addresses independent variables that include customer commitment, customer privacy, customer trust, ease of use, E-service quality. The framework also considers e-crm as a mediating variable and customer loyalty as a dependent variable. Therefore, the proposed model that incorporates the variables to be studied is illustrated in Figure 1 Customer commitment Customer privacy Customer trust Ease of use E-CRM Customer loyalty E-service quality 3. Methodology 3.1 Population and Sample The study s target population consisted of mobile phone users in Jordan. This study sample was students studying in Jordan University. A stratified sampling
was used to select approximately equal numbers of customers from each university. Questionnaires distributed to five universities in the southern, northern, and central states of Jordan. Furthermore, students were selected because the student population is known for its technological expertise (Hair et., 2007; Coakes et al., 2006). In addition, there were 500 questionnaires distributed to five universities in Jordan, in Arabic languages. However, only 450 were completed as usable questionnaires and were used for data analysis in this research. According to Sekaran (2006), 450 responses are considered as an acceptable number for researchers to proceed with data analysis. 3.2 Data Collection Procedures Data were collected by Self-administered questionnaires from Jordan university students in Jordan. Once the entire respondents have been identified, the next procedure in the study involved distribution of the questionnaires. Data were collected through survey whereby the questionnaires were distributed to the respondents personally by hand. Furthermore, all respondent were given two weeks to complete the questionnaires and were asked to submit the completed ones to the researcher by hand. Majority of the questionnaires (450) were collected immediately on the same day of distribution or after the given time (two weeks). 3.2 Data Analysis Data analysis involved steps such as coding the responses, cleaning, screening the data and selecting the appropriate data analysis strategy (Churchill & Lacobucci, 2004; Sekaran, 2000). For the purpose of data analysis and hypotheses testing, several statistical tools and methods were employed from SPSS software version 15. This includes factor and reliability analysis to test the goodness of the measures, descriptive statistics to describe the characteristic of respondents, and to compare the extent of e-crm performance performed by the respondents between different demographic profiles. Correlation analysis is used to describe the relation between the variable and regression analysis is utilized to test the influence e-crm performance on customer loyalty, and the influence of antecedent factors on e-crm performance. 4. Results and Discussion 4.1 Descriptive Analysis Subsequent to the assessment of normality is descriptive statistics analysis. Descriptive statistics include the minimum and maximum, means, standard deviation and variance for the interval-scaled variables. The researcher depended on the data from the questionnaire to determine the level of e-crm performance and customer loyalty perceived by subscribers in mobile phone service in Jordan. As shown in Table 1, the mean values for most of the variables are at the range of 3.82 to 3.97. This indicates that most respondents share slightly similar opinions on customer factors, technology factors, and process
factor and customer loyalty. Most of the standard deviation were less than 1.00, indicating that the variation on respondents opinions were small. Table 1: Descriptive Statistics of Main Variables (n = 450) Variables Total items Min Max Mean Std. deviation ECRM 6 1.00 5.00 3.9645. 46265 Customer loyalty 6 1.00 5.00 3.8439.61878 Customer commitment 6 1.00 5.00 3.8448.56692 Customer privacy 6 1.00 5.00 3.9718.65252 Customer trust 6 1.00 5.00 3.94571.48037 Ease of use 6 1.00 5.00 3.8292.62278 E-service quality 6 1.00 5.00 3.9473.508870 4.2 Scale Reliabilities Cronbach s alpha can be considered as a perfectly adequate indication of the internal consistency, and thus of reliability (Sekaran, 2007). It is the most widely used indicator. The generally agreed upon most acceptable value for Cronbach s alpha is 0.70, although it may decrease to 0.50 in exploratory research (Hair et al., 2007). Table 2 below summarizes the reliability test of the measures after taking in to the consideration of deleted items. As demonstrate, the Cronbach s alphas of the measures were all above the lower limit of acceptability that is more than 0.50. For this reason, all measures were highly reliable and acceptable, and thus, providing strong support for the variable components. Table 2: Reliability Analysis Factors Number of items Cronbach s Alpha Customer loyalty 8.865 E-CRM performance 6.827 Customer commitment 6.803 Customer privacy 6.790 Customer trust 6.752 Ease of use 6.730 E-service quality 6.700 4.3 Correlation Analysis Table 3 shows a summary of the results from correlation analysis. The computation of the Pearson correlation coefficient was performed to understand the relationship among all variables in the study. The correlation coefficients (r) given in Table 3 indicate the strength of the relationship between the variables and the correlation coefficient for all latent variables were found under the threshold of.90 (Hair et al., 2006). Overall correlation values of the variable as shown in Table 3 showed correlation coefficients with values above.5, which normally indicate high associations between variables. With regard to the relationship between e-crm performance and customer loyalty, the correlation is highly significant at.67. Cohen (1988) suggests that if r score is above.50 the correlation between the two variables are considered largely correlated. It gives indication that e-crm performance is one of the variables influencing customer loyalty. On the other hand, majority of the antecedents are significantly correlated
with e-crm performance with values above.5. According to Cohen (1988) and Pallant (2007), a coefficient of more than.50 means largely correlated variables. Table: 3 Pearson Correlations of Study Variables ECRM COM CPRV TRST EOUS ESQ ECRM 1.0 COM.82 (**) 1.0 CPRV.65 (**).64(**) 1.0 TRST.77 (**). 56(**).66(**) 1.0 EOUS.79 (**).56(**).61(**) 53(**) 1.0 ESQ.855(**).52(**).50(**).50(**).61(**) 1.0 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Note: Electronic customer relationship management performance= ECRM, customer commitment=crm, customer privacy=cprv, customer trust=trst, Ease of use=eous, electronic service quality=esq 4.4 Regression Analysis To determine the effect of customer commitment, customer privacy, customer trust, Ease of use, and electronic service quality, on E-CRM performance? regression analysis was undertaken on the antecedent factors and E-CRM performance. The major assumptions take in our consideration are sample size, Multicollinearity and singularity, Outliers, Normality, linearity, homoscedasticity. All these assumptions have been tested to make this data suitable for regression analysis. Multiple regressions are used to explain the relationship between a single dependent (criterion) variable and several independent (predictor) variables. There are a few approaches that are used for multiple regression analysis such as standard regression, hierarchical or sequential, and stepwise regression (Palant, 2001; 2007). Table: 4. Summary of Multiple Regressions Analysis for Factors Influencing e-crm Performance Model Independent variable B Beta Sig customer commitment 0.261 0.199 0.000 customer privacy 0.332 0.326 0.000 customer trust 0.254.049 0.000 Ease of use 0.335 0.293 0.000 electronic service quality 0.183 0.122 0.002 Note; DV= e-crm; R =.863 Adjusted R Square =.744 R Square =.744
Table: 5.The influence of ECRM Performance on Customer loyalty. Unstandardized coefficient Standardized coefficient Collinearity statistics Model B Std. Beta t Sig. error (Constant) -.462.120-3.859.000 ECRM.443.039.393 11.290.000.398 2.510 R =.862 Adjusted R Square =.741 R Square =.743 df = 4 F statistics = 385.300 Sig = 0.00. Note. Dependent variable: customer loyalty 4.5 Hypothesis Test The results of Hypotheses 1 demonstrates that customer commitment is significantly and positively related to ECRM for the total sample (β= 0.261, p=.000). Therefore, the results support Hypothesis 1. Regarding Hypotheses 2, the data indicate that customer privacy is significantly related to ECRM for the total sample (β=. 0.332, p=.000). Therefore, the results support Hypothesis 2. The findings of Hypotheses 3 indicate that customer trust is significantly and positively related to ECRM for the total sample (β= 0.254, p=.000). Therefore, the results support Hypothesis 3. Also, the findings of Hypotheses 4, the data indicate that Ease of use is significantly related to ECRM for the total sample (β= 0.335, p=.000). Therefore, Hypothesis 4 is support. Furthermore, the findings of Hypotheses 5 indicate that electronic service quality is significantly and positively related to ECRM for the total sample (β= 0.183, p=0.002). Finally, the findings of Hypotheses 6 indicate that ECRM is significantly and positively related to customer loyalty for the total sample (β=.443, p=0.000) 5. Conclusions This research proved that there are five factors that have a significant relationship with E-CRM performance; these factors are customer commitment, customer privacy, customer trust, ease of use, electronic service quality. The possible reason is that University students have numerous services to enjoy more than mobile phone service, such as, sports, computer, internet services, and libraries.etc. Therefore, students use the mobile phone for other objectives as making and receiving calls, SMS service, discussing studying issues but not for enjoyment. Also, most of the students use old types of mobile phone and this mobile phone have no facilities for internet serving. The findings of this research give numerous implications for mobile service providers and marketing managers with regard to how to plan and market services that will be considered valuable by customers and used in the future. Additionally, the present research regarded as important grounds for formulating and implementing e-crm performance in evaluating service providers to assign proportionate amount of resources to achieve sustainable customer loyalty. Regarding the variables that influencing E-CRM performance. The present study suggests numerous variables as important determinants of E-CRM performance.
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